Overview

Dataset statistics

Number of variables21
Number of observations10127
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory168.0 B

Variable types

Numeric17
Categorical4

Warnings

credit_limit is highly correlated with avg_open_to_buyHigh correlation
avg_open_to_buy is highly correlated with credit_limitHigh correlation
client_num has unique values Unique
dependent_count has 904 (8.9%) zeros Zeros
education has 1519 (15.0%) zeros Zeros
income_category has 1112 (11.0%) zeros Zeros
contacts_count_12_mon has 399 (3.9%) zeros Zeros
total_revolving_bal has 2470 (24.4%) zeros Zeros
avg_utilization_ratio has 2470 (24.4%) zeros Zeros

Reproduction

Analysis started2021-01-20 19:24:52.992363
Analysis finished2021-01-20 19:29:45.270750
Duration4 minutes and 52.28 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

client_num
Real number (ℝ≥0)

UNIQUE

Distinct10127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean739177606.3
Minimum708082083
Maximum828343083
Zeros0
Zeros (%)0.0%
Memory size79.2 KiB
2021-01-20T21:29:45.887663image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum708082083
5-th percentile709120390.5
Q1713036770.5
median717926358
Q3773143533
95-th percentile814212033
Maximum828343083
Range120261000
Interquartile range (IQR)60106762.5

Descriptive statistics

Standard deviation36903783.45
Coefficient of variation (CV)0.04992546194
Kurtosis-0.6156397044
Mean739177606.3
Median Absolute Deviation (MAD)6347700
Skewness0.9956010103
Sum7.485651619 × 1012
Variance1.361889233 × 1015
MonotocityNot monotonic
2021-01-20T21:29:46.948217image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7800975331
 
< 0.1%
7200490831
 
< 0.1%
7173767581
 
< 0.1%
7205983081
 
< 0.1%
7199306581
 
< 0.1%
7168054081
 
< 0.1%
8199979831
 
< 0.1%
7781919331
 
< 0.1%
8241656581
 
< 0.1%
7712207581
 
< 0.1%
Other values (10117)10117
99.9%
ValueCountFrequency (%)
7080820831
< 0.1%
7080832831
< 0.1%
7080845581
< 0.1%
7080854581
< 0.1%
7080869581
< 0.1%
ValueCountFrequency (%)
8283430831
< 0.1%
8282989081
< 0.1%
8282949331
< 0.1%
8282918581
< 0.1%
8282883331
< 0.1%

exists
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
1
8500 
0
1627 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10127
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
18500
83.9%
01627
 
16.1%
2021-01-20T21:29:48.796936image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category
2021-01-20T21:29:49.349748image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
ValueCountFrequency (%)
18500
83.9%
01627
 
16.1%

Most occurring characters

ValueCountFrequency (%)
18500
83.9%
01627
 
16.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10127
100.0%

Most frequent character per category

ValueCountFrequency (%)
18500
83.9%
01627
 
16.1%

Most occurring scripts

ValueCountFrequency (%)
Common10127
100.0%

Most frequent character per script

ValueCountFrequency (%)
18500
83.9%
01627
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10127
100.0%

Most frequent character per block

ValueCountFrequency (%)
18500
83.9%
01627
 
16.1%

age
Real number (ℝ≥0)

Distinct45
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.3259603
Minimum26
Maximum73
Zeros0
Zeros (%)0.0%
Memory size79.2 KiB
2021-01-20T21:29:49.914580image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile33
Q141
median46
Q352
95-th percentile60
Maximum73
Range47
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.016814033
Coefficient of variation (CV)0.1730523011
Kurtosis-0.2886199153
Mean46.3259603
Median Absolute Deviation (MAD)6
Skewness-0.03360501632
Sum469143
Variance64.26930723
MonotocityNot monotonic
2021-01-20T21:29:50.746805image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
44500
 
4.9%
49495
 
4.9%
46490
 
4.8%
45486
 
4.8%
47479
 
4.7%
43473
 
4.7%
48472
 
4.7%
50452
 
4.5%
42426
 
4.2%
51398
 
3.9%
Other values (35)5456
53.9%
ValueCountFrequency (%)
2678
0.8%
2732
0.3%
2829
 
0.3%
2956
0.6%
3070
0.7%
ValueCountFrequency (%)
731
 
< 0.1%
701
 
< 0.1%
682
< 0.1%
674
< 0.1%
662
< 0.1%

female
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
1
5358 
0
4769 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10127
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0
ValueCountFrequency (%)
15358
52.9%
04769
47.1%
2021-01-20T21:29:52.183918image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category
2021-01-20T21:29:52.622565image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
ValueCountFrequency (%)
15358
52.9%
04769
47.1%

Most occurring characters

ValueCountFrequency (%)
15358
52.9%
04769
47.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10127
100.0%

Most frequent character per category

ValueCountFrequency (%)
15358
52.9%
04769
47.1%

Most occurring scripts

ValueCountFrequency (%)
Common10127
100.0%

Most frequent character per script

ValueCountFrequency (%)
15358
52.9%
04769
47.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10127
100.0%

Most frequent character per block

ValueCountFrequency (%)
15358
52.9%
04769
47.1%

dependent_count
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.346203219
Minimum0
Maximum5
Zeros904
Zeros (%)8.9%
Memory size79.2 KiB
2021-01-20T21:29:53.022151image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.298908349
Coefficient of variation (CV)0.5536214162
Kurtosis-0.6830166531
Mean2.346203219
Median Absolute Deviation (MAD)1
Skewness-0.02082553562
Sum23760
Variance1.687162899
MonotocityNot monotonic
2021-01-20T21:29:53.613021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
32732
27.0%
22655
26.2%
11838
18.1%
41574
15.5%
0904
 
8.9%
5424
 
4.2%
ValueCountFrequency (%)
0904
 
8.9%
11838
18.1%
22655
26.2%
32732
27.0%
41574
15.5%
ValueCountFrequency (%)
5424
 
4.2%
41574
15.5%
32732
27.0%
22655
26.2%
11838
18.1%

education
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.601955169
Minimum0
Maximum6
Zeros1519
Zeros (%)15.0%
Memory size79.2 KiB
2021-01-20T21:29:54.214905image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.700416503
Coefficient of variation (CV)0.6535149118
Kurtosis-1.005170191
Mean2.601955169
Median Absolute Deviation (MAD)1
Skewness0.002252395754
Sum26350
Variance2.891416284
MonotocityNot monotonic
2021-01-20T21:29:54.905920image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
43128
30.9%
22013
19.9%
01519
15.0%
11487
14.7%
31013
 
10.0%
5516
 
5.1%
6451
 
4.5%
ValueCountFrequency (%)
01519
15.0%
11487
14.7%
22013
19.9%
31013
 
10.0%
43128
30.9%
ValueCountFrequency (%)
6451
 
4.5%
5516
 
5.1%
43128
30.9%
31013
 
10.0%
22013
19.9%

marital_status
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
2
4687 
1
3943 
0
749 
3
748 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10127
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row0
5th row2
ValueCountFrequency (%)
24687
46.3%
13943
38.9%
0749
 
7.4%
3748
 
7.4%
2021-01-20T21:29:56.598410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category
2021-01-20T21:29:57.092137image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
ValueCountFrequency (%)
24687
46.3%
13943
38.9%
0749
 
7.4%
3748
 
7.4%

Most occurring characters

ValueCountFrequency (%)
24687
46.3%
13943
38.9%
0749
 
7.4%
3748
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10127
100.0%

Most frequent character per category

ValueCountFrequency (%)
24687
46.3%
13943
38.9%
0749
 
7.4%
3748
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common10127
100.0%

Most frequent character per script

ValueCountFrequency (%)
24687
46.3%
13943
38.9%
0749
 
7.4%
3748
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII10127
100.0%

Most frequent character per block

ValueCountFrequency (%)
24687
46.3%
13943
38.9%
0749
 
7.4%
3748
 
7.4%

income_category
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.072578256
Minimum0
Maximum5
Zeros1112
Zeros (%)11.0%
Memory size79.2 KiB
2021-01-20T21:29:57.699029image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.461928972
Coefficient of variation (CV)0.7053673212
Kurtosis-0.8568990602
Mean2.072578256
Median Absolute Deviation (MAD)1
Skewness0.4807421784
Sum20989
Variance2.137236321
MonotocityNot monotonic
2021-01-20T21:29:58.384036image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
13561
35.2%
21790
17.7%
31535
15.2%
41402
 
13.8%
01112
 
11.0%
5727
 
7.2%
ValueCountFrequency (%)
01112
 
11.0%
13561
35.2%
21790
17.7%
31535
15.2%
41402
 
13.8%
ValueCountFrequency (%)
5727
 
7.2%
41402
 
13.8%
31535
15.2%
21790
17.7%
13561
35.2%

card
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
1
9436 
2
 
555
3
 
116
4
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10127
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
19436
93.2%
2555
 
5.5%
3116
 
1.1%
420
 
0.2%
2021-01-20T21:29:59.910285image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category
2021-01-20T21:30:00.438057image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
ValueCountFrequency (%)
19436
93.2%
2555
 
5.5%
3116
 
1.1%
420
 
0.2%

Most occurring characters

ValueCountFrequency (%)
19436
93.2%
2555
 
5.5%
3116
 
1.1%
420
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10127
100.0%

Most frequent character per category

ValueCountFrequency (%)
19436
93.2%
2555
 
5.5%
3116
 
1.1%
420
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common10127
100.0%

Most frequent character per script

ValueCountFrequency (%)
19436
93.2%
2555
 
5.5%
3116
 
1.1%
420
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII10127
100.0%

Most frequent character per block

ValueCountFrequency (%)
19436
93.2%
2555
 
5.5%
3116
 
1.1%
420
 
0.2%

months_on_book
Real number (ℝ≥0)

Distinct44
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.9284092
Minimum13
Maximum56
Zeros0
Zeros (%)0.0%
Memory size79.2 KiB
2021-01-20T21:30:01.225218image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile22
Q131
median36
Q340
95-th percentile50
Maximum56
Range43
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.986416331
Coefficient of variation (CV)0.2222869453
Kurtosis0.4001001202
Mean35.9284092
Median Absolute Deviation (MAD)4
Skewness-0.1065653599
Sum363847
Variance63.78284581
MonotocityNot monotonic
2021-01-20T21:30:02.172613image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
362463
24.3%
37358
 
3.5%
34353
 
3.5%
38347
 
3.4%
39341
 
3.4%
40333
 
3.3%
31318
 
3.1%
35317
 
3.1%
33305
 
3.0%
30300
 
3.0%
Other values (34)4692
46.3%
ValueCountFrequency (%)
1370
0.7%
1416
 
0.2%
1534
0.3%
1629
0.3%
1739
0.4%
ValueCountFrequency (%)
56103
1.0%
5542
0.4%
5453
0.5%
5378
0.8%
5262
0.6%

total_relationship_count
Real number (ℝ≥0)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.812580231
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Memory size79.2 KiB
2021-01-20T21:30:02.984806image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.554407865
Coefficient of variation (CV)0.4077049586
Kurtosis-1.006130507
Mean3.812580231
Median Absolute Deviation (MAD)1
Skewness-0.162452415
Sum38610
Variance2.416183812
MonotocityNot monotonic
2021-01-20T21:30:03.747928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
32305
22.8%
41912
18.9%
51891
18.7%
61866
18.4%
21243
12.3%
1910
 
9.0%
ValueCountFrequency (%)
1910
 
9.0%
21243
12.3%
32305
22.8%
41912
18.9%
51891
18.7%
ValueCountFrequency (%)
61866
18.4%
51891
18.7%
41912
18.9%
32305
22.8%
21243
12.3%

months_inactive_12_mon
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.341167177
Minimum0
Maximum6
Zeros29
Zeros (%)0.3%
Memory size79.2 KiB
2021-01-20T21:30:04.461979image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.010622399
Coefficient of variation (CV)0.4316745978
Kurtosis1.098522614
Mean2.341167177
Median Absolute Deviation (MAD)1
Skewness0.633061129
Sum23709
Variance1.021357634
MonotocityNot monotonic
2021-01-20T21:30:05.229106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
33846
38.0%
23282
32.4%
12233
22.0%
4435
 
4.3%
5178
 
1.8%
6124
 
1.2%
029
 
0.3%
ValueCountFrequency (%)
029
 
0.3%
12233
22.0%
23282
32.4%
33846
38.0%
4435
 
4.3%
ValueCountFrequency (%)
6124
 
1.2%
5178
 
1.8%
4435
 
4.3%
33846
38.0%
23282
32.4%

contacts_count_12_mon
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.455317468
Minimum0
Maximum6
Zeros399
Zeros (%)3.9%
Memory size79.2 KiB
2021-01-20T21:30:06.033292image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.106225143
Coefficient of variation (CV)0.4505426109
Kurtosis0.0008626566254
Mean2.455317468
Median Absolute Deviation (MAD)1
Skewness0.01100562622
Sum24865
Variance1.223734066
MonotocityNot monotonic
2021-01-20T21:30:06.723306image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
33380
33.4%
23227
31.9%
11499
14.8%
41392
13.7%
0399
 
3.9%
5176
 
1.7%
654
 
0.5%
ValueCountFrequency (%)
0399
 
3.9%
11499
14.8%
23227
31.9%
33380
33.4%
41392
13.7%
ValueCountFrequency (%)
654
 
0.5%
5176
 
1.7%
41392
13.7%
33380
33.4%
23227
31.9%

credit_limit
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6205
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8631.953698
Minimum1438.3
Maximum34516
Zeros0
Zeros (%)0.0%
Memory size79.2 KiB
2021-01-20T21:30:07.731786image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1438.3
5-th percentile1438.51
Q12555
median4549
Q311067.5
95-th percentile34516
Maximum34516
Range33077.7
Interquartile range (IQR)8512.5

Descriptive statistics

Standard deviation9088.77665
Coefficient of variation (CV)1.052922313
Kurtosis1.808989336
Mean8631.953698
Median Absolute Deviation (MAD)2593
Skewness1.666725808
Sum87415795.1
Variance82605861
MonotocityNot monotonic
2021-01-20T21:30:08.818384image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34516508
 
5.0%
1438.3507
 
5.0%
1598718
 
0.2%
995918
 
0.2%
2398112
 
0.1%
622411
 
0.1%
249011
 
0.1%
373511
 
0.1%
746910
 
0.1%
20698
 
0.1%
Other values (6195)9013
89.0%
ValueCountFrequency (%)
1438.3507
5.0%
14392
 
< 0.1%
14401
 
< 0.1%
14412
 
< 0.1%
14421
 
< 0.1%
ValueCountFrequency (%)
34516508
5.0%
344961
 
< 0.1%
344581
 
< 0.1%
344271
 
< 0.1%
341981
 
< 0.1%

total_revolving_bal
Real number (ℝ≥0)

ZEROS

Distinct1974
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.814061
Minimum0
Maximum2517
Zeros2470
Zeros (%)24.4%
Memory size79.2 KiB
2021-01-20T21:30:10.219444image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1359
median1276
Q31784
95-th percentile2517
Maximum2517
Range2517
Interquartile range (IQR)1425

Descriptive statistics

Standard deviation814.9873352
Coefficient of variation (CV)0.7008750257
Kurtosis-1.145991782
Mean1162.814061
Median Absolute Deviation (MAD)591
Skewness-0.1488372503
Sum11775818
Variance664204.3566
MonotocityNot monotonic
2021-01-20T21:30:11.377149image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02470
 
24.4%
2517508
 
5.0%
196512
 
0.1%
148012
 
0.1%
172011
 
0.1%
166411
 
0.1%
143411
 
0.1%
154210
 
0.1%
117510
 
0.1%
156010
 
0.1%
Other values (1964)7062
69.7%
ValueCountFrequency (%)
02470
24.4%
1321
 
< 0.1%
1341
 
< 0.1%
1451
 
< 0.1%
1541
 
< 0.1%
ValueCountFrequency (%)
2517508
5.0%
25143
 
< 0.1%
25131
 
< 0.1%
25122
 
< 0.1%
25111
 
< 0.1%

avg_open_to_buy
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6813
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7469.139637
Minimum3
Maximum34516
Zeros0
Zeros (%)0.0%
Memory size79.2 KiB
2021-01-20T21:30:12.934442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile480.3
Q11324.5
median3474
Q39859
95-th percentile32183.4
Maximum34516
Range34513
Interquartile range (IQR)8534.5

Descriptive statistics

Standard deviation9090.685324
Coefficient of variation (CV)1.217099394
Kurtosis1.798617296
Mean7469.139637
Median Absolute Deviation (MAD)2665
Skewness1.661696546
Sum75639977.1
Variance82640559.65
MonotocityNot monotonic
2021-01-20T21:30:14.295076image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1438.3324
 
3.2%
3451698
 
1.0%
3199926
 
0.3%
7878
 
0.1%
9537
 
0.1%
7017
 
0.1%
4637
 
0.1%
7137
 
0.1%
7406
 
0.1%
9336
 
0.1%
Other values (6803)9631
95.1%
ValueCountFrequency (%)
31
< 0.1%
101
< 0.1%
142
< 0.1%
151
< 0.1%
241
< 0.1%
ValueCountFrequency (%)
3451698
1.0%
343621
 
< 0.1%
343021
 
< 0.1%
343001
 
< 0.1%
342971
 
< 0.1%

total_amt_chng_q4_q1
Real number (ℝ≥0)

Distinct1158
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7599406537
Minimum0
Maximum3.397
Zeros5
Zeros (%)< 0.1%
Memory size79.2 KiB
2021-01-20T21:30:17.010068image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.463
Q10.631
median0.736
Q30.859
95-th percentile1.103
Maximum3.397
Range3.397
Interquartile range (IQR)0.228

Descriptive statistics

Standard deviation0.2192067692
Coefficient of variation (CV)0.288452484
Kurtosis9.993501179
Mean0.7599406537
Median Absolute Deviation (MAD)0.114
Skewness1.732063411
Sum7695.919
Variance0.04805160768
MonotocityNot monotonic
2021-01-20T21:30:18.227861image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.79136
 
0.4%
0.74334
 
0.3%
0.71234
 
0.3%
0.73533
 
0.3%
0.71833
 
0.3%
0.72232
 
0.3%
0.74432
 
0.3%
0.69932
 
0.3%
0.76731
 
0.3%
0.6931
 
0.3%
Other values (1148)9799
96.8%
ValueCountFrequency (%)
05
< 0.1%
0.011
 
< 0.1%
0.0181
 
< 0.1%
0.0461
 
< 0.1%
0.0612
 
< 0.1%
ValueCountFrequency (%)
3.3971
< 0.1%
3.3551
< 0.1%
2.6751
< 0.1%
2.5941
< 0.1%
2.3681
< 0.1%

total_trans_amt
Real number (ℝ≥0)

Distinct5033
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4404.086304
Minimum510
Maximum18484
Zeros0
Zeros (%)0.0%
Memory size79.2 KiB
2021-01-20T21:30:19.463676image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum510
5-th percentile1283.3
Q12155.5
median3899
Q34741
95-th percentile14212
Maximum18484
Range17974
Interquartile range (IQR)2585.5

Descriptive statistics

Standard deviation3397.129254
Coefficient of variation (CV)0.7713584656
Kurtosis3.894023406
Mean4404.086304
Median Absolute Deviation (MAD)1308
Skewness2.041003403
Sum44600182
Variance11540487.17
MonotocityNot monotonic
2021-01-20T21:30:20.655429image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
425311
 
0.1%
450911
 
0.1%
222910
 
0.1%
451810
 
0.1%
48699
 
0.1%
40429
 
0.1%
43139
 
0.1%
42209
 
0.1%
44989
 
0.1%
40379
 
0.1%
Other values (5023)10031
99.1%
ValueCountFrequency (%)
5101
< 0.1%
5301
< 0.1%
5631
< 0.1%
5691
< 0.1%
5941
< 0.1%
ValueCountFrequency (%)
184841
< 0.1%
179951
< 0.1%
177441
< 0.1%
176341
< 0.1%
176281
< 0.1%

total_trans_ct
Real number (ℝ≥0)

Distinct126
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.85869458
Minimum10
Maximum139
Zeros0
Zeros (%)0.0%
Memory size79.2 KiB
2021-01-20T21:30:22.077521image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile28
Q145
median67
Q381
95-th percentile105
Maximum139
Range129
Interquartile range (IQR)36

Descriptive statistics

Standard deviation23.47257045
Coefficient of variation (CV)0.3619032206
Kurtosis-0.3671632411
Mean64.85869458
Median Absolute Deviation (MAD)17
Skewness0.1536730685
Sum656824
Variance550.9615635
MonotocityNot monotonic
2021-01-20T21:30:23.771011image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81208
 
2.1%
75203
 
2.0%
71203
 
2.0%
82202
 
2.0%
69202
 
2.0%
76198
 
2.0%
77197
 
1.9%
70193
 
1.9%
78190
 
1.9%
74190
 
1.9%
Other values (116)8141
80.4%
ValueCountFrequency (%)
104
< 0.1%
112
 
< 0.1%
124
< 0.1%
135
< 0.1%
149
0.1%
ValueCountFrequency (%)
1391
 
< 0.1%
1381
 
< 0.1%
1341
 
< 0.1%
1321
 
< 0.1%
1316
0.1%

total_ct_chng_q4_q1
Real number (ℝ≥0)

Distinct830
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7122223758
Minimum0
Maximum3.714
Zeros7
Zeros (%)0.1%
Memory size79.2 KiB
2021-01-20T21:30:25.168066image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.368
Q10.582
median0.702
Q30.818
95-th percentile1.069
Maximum3.714
Range3.714
Interquartile range (IQR)0.236

Descriptive statistics

Standard deviation0.2380860913
Coefficient of variation (CV)0.3342861716
Kurtosis15.6892929
Mean0.7122223758
Median Absolute Deviation (MAD)0.119
Skewness2.064030568
Sum7212.676
Variance0.05668498689
MonotocityNot monotonic
2021-01-20T21:30:26.577138image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.667171
 
1.7%
1166
 
1.6%
0.5161
 
1.6%
0.75156
 
1.5%
0.6113
 
1.1%
0.8101
 
1.0%
0.71492
 
0.9%
0.83385
 
0.8%
0.77869
 
0.7%
0.62563
 
0.6%
Other values (820)8950
88.4%
ValueCountFrequency (%)
07
0.1%
0.0281
 
< 0.1%
0.0291
 
< 0.1%
0.0381
 
< 0.1%
0.0531
 
< 0.1%
ValueCountFrequency (%)
3.7141
< 0.1%
3.5711
< 0.1%
3.51
< 0.1%
3.251
< 0.1%
32
< 0.1%

avg_utilization_ratio
Real number (ℝ≥0)

ZEROS

Distinct964
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2748935519
Minimum0
Maximum0.999
Zeros2470
Zeros (%)24.4%
Memory size79.2 KiB
2021-01-20T21:30:28.390805image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.023
median0.176
Q30.503
95-th percentile0.793
Maximum0.999
Range0.999
Interquartile range (IQR)0.48

Descriptive statistics

Standard deviation0.2756914693
Coefficient of variation (CV)1.002902641
Kurtosis-0.7949719515
Mean0.2748935519
Median Absolute Deviation (MAD)0.176
Skewness0.7180079968
Sum2783.847
Variance0.07600578622
MonotocityNot monotonic
2021-01-20T21:30:30.391749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02470
 
24.4%
0.07344
 
0.4%
0.05733
 
0.3%
0.04832
 
0.3%
0.0630
 
0.3%
0.04529
 
0.3%
0.06129
 
0.3%
0.06928
 
0.3%
0.05928
 
0.3%
0.05327
 
0.3%
Other values (954)7377
72.8%
ValueCountFrequency (%)
02470
24.4%
0.0041
 
< 0.1%
0.0051
 
< 0.1%
0.0063
 
< 0.1%
0.0071
 
< 0.1%
ValueCountFrequency (%)
0.9991
< 0.1%
0.9951
< 0.1%
0.9941
< 0.1%
0.9921
< 0.1%
0.991
< 0.1%

Interactions

2021-01-20T21:25:13.007386image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:13.898726image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:14.660333image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:15.424121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:16.220495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:16.936095image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:17.732535image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:18.668013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:19.683026image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:21.062056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:21.971331image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:23.008859image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:24.173918image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:25.279545image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:26.350265image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:27.375028image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:28.366091image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:29.468638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:30.506979image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:31.654939image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:32.570630image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:33.365118image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:34.160679image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:34.943436image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:35.730049image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:36.670967image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:37.499688image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:38.440570image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:39.383956image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:40.184070image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:41.383807image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:42.585573image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:43.789003image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:44.570050image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:45.304572image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:46.095588image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:46.903969image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:47.632959image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:48.698551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:49.527770image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:50.739552image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:51.652895image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:52.486123image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:53.440524image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:54.337847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:55.209130image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:56.051366image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:56.946681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:57.792928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:58.643179image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:25:59.478404image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:00.369716image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:01.256020image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:02.094255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:02.989569image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:03.847833image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:04.749158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:05.720589image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:06.888306image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:08.103095image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:09.122592image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:10.071984image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:11.396936image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:12.473520image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:13.382856image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:14.288188image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:15.097377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:15.954641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:16.884004image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:17.750278image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:18.921009image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:19.889432image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:22.555357image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:24.443130image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:25.358476image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:26.355939image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:27.298326image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:28.695380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:29.613731image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:30.550109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:31.469458image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:32.312703image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-01-20T21:26:33.080832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-01-20T21:30:42.270225image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-01-20T21:30:44.968189image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-01-20T21:29:40.259380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-01-20T21:29:43.959821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

client_numexistsagefemaledependent_counteducationmarital_statusincome_categorycardmonths_on_booktotal_relationship_countmonths_inactive_12_moncontacts_count_12_moncredit_limittotal_revolving_balavg_open_to_buytotal_amt_chng_q4_q1total_trans_amttotal_trans_cttotal_ct_chng_q4_q1avg_utilization_ratio
07688053831450322413951312691.077711914.01.3351144421.6250.061
1818770008149154111446128256.08647392.01.5411291333.7140.105
2713982108151034231364103418.003418.02.5941887202.3330.000
3769911858140142011343413313.02517796.01.4051171202.3330.760
4709106358140031241215104716.004716.02.175816282.5000.000
5713061558144024221363124010.012472763.01.3761088240.8460.311
68103472081510402534661334516.0226432252.01.9751330310.7220.066
78189062081320020422722229081.0139627685.02.2041538360.7140.048
87109305081370311413652022352.0251719835.03.3551350241.1820.113
97196615581480241313663311656.016779979.01.5241441320.8820.144

Last rows

client_numexistsagefemaledependent_counteducationmarital_statusincome_categorycardmonths_on_booktotal_relationship_countmonths_inactive_12_moncontacts_count_12_moncredit_limittotal_revolving_balavg_open_to_buytotal_amt_chng_q4_q1total_trans_amttotal_trans_cttotal_ct_chng_q4_q1avg_utilization_ratio
101177125034081570242314063417925.0190916016.00.712174981110.8200.106
10118713755458050010031366349959.09529007.00.82510310631.1000.096
101197168936830551311014743314657.0251712140.00.1666009530.5140.172
101207108411831540121413452013940.0210911831.00.660155771140.7540.151
10121713899383156114111504143688.06063082.00.570145961200.7910.164
10122772366833150024121403234003.018512152.00.703154761170.8570.462
10123710638233041020321254234277.021862091.00.8048764690.6830.511
10124716506083044112211365345409.005409.00.81910291600.8180.000
10125717406983030024021364335281.005281.00.5358395620.7220.000
101267143372330431242122562410388.019618427.00.70310294610.6490.189